Electronic data processing system and method of using an electronic data processing system for automatically determining a risk indicator value

ABSTRACT

An electronic data processing system for automatically determining a risk indicator value based on a number of risk parameters, for evaluating a risk involved with performing a transaction between a client and a transaction provider in said data processing system.

The invention relates to an electronic data processing system and amethod of using an electronic data processing system for automaticallydetermining a risk indicator value based on a number of risk parameters,for evaluating the risk-involved with performing a transaction between aclient and a transaction provider in said data processing system.

For monitoring and predicting the success of transactions, such ascarrying out technical processes having a large number of variable inputparameters but also for financial and commercial transactions, earlywarning risk indicators systems have been developed for estimating arisk involved with performing said process or transaction between theclient and the transaction provider.

Such early warning risk indicator systems of the prior art essentiallyare based on a set of characteristics and attributes of theprocess/transaction for determining a high, medium or low risk businessengagement (e.g. a business engagement, such as a loan application).

Prior art examples of such early warning risk indicator systems aredescribed e.g. in U.S. Pat. No. 6,202,053 B1, U.S. Pat. No. 6,311,169B2, and the Journal of Commercial Landing, June 1995, pages 10 to 16“How the RMA/Fair, Isaac Credit-scoring model was build” by LatimerAsch.

U.S. Pat. No. 6,202,053 B1 describes bow, to assess the credit risk ofan individual, a financial institution will develop a score for eachcredit applicant based on certain information. The applicant receivespoints for each item of information analysed by the financialinstitution. The amount of points awarded for each item, the itemsactually analysed, and the score necessary for approval may vary. Thisscore awarded is used to evaluate a risk involved in performing acertain transaction. In other words, the decision to approve or deny anapplicants request for e.g. a bank card or another type of transactionis based on a scoring system. The scoring system used to evaluate eachapplicant and the minimum score required for approval wasapplied-uniformly by a financial institution to all its applicants. Theuse of such a scoring system for evaluating a risk involved with atransaction is rather superficial and could be made more secure byeither monitoring the financial behaviour of the approved client afterapproval or increasing the score required for approval. The firstalternative would require an increase in costs and efforts whereas thesecond alternative might lead to unnecessarily declining a large numberof the clients.

Therefore, U.S. Pat. No. 6,202,053 B1 proposes to develop a segmentationtree, building a client's score card for each segment, grouping clientsinto sub-populations corresponding to each segment, and applying theclient's score card to the applicants within the corresponding segment.Using an automated system to implement the generation of the clientsscore cards and scoring the applications further lowers costs and effortof assessing a risk involved with a transaction.

The general background of the RMA/Fair, Isaac, credit-scoring model isdescribed in the above mentioned article by Latimer Asch. The model issuitable for e.g. reducing the time spent for processing small businessloan applications using an automated solution which is based on apooled-data score card. A scorecard is a tool used to calculate the riskassociated with a credit application. It calculates the credit riskbased on multiple items of information called characteristics.Characteristics can come from several sources, including the creditapplication and consumer and business credit reports. Eachcharacteristic is divided into two or more possible responses known asattributes. A numerical-score is associated with each attribute, so forany credit application the numerical attribute values for allcharacteristics can be added together to provide a total score. Scoring,in principal, uses the same data a loan officer uses in his or herjudgmental, or nonscoring, decision process. But scoring is faster, moreobjective, and more consistent. With the current regulatory pressure toprovide more small business loans, prospective lenders need efficient,time-saving, cost-cutting tools. With credit-scoring, a lender canincrease the number of approved applications without increasing risk,time, or other resources.

The scoring system described above, although performed automatically ina data processing system and being able to handle a relatively largenumber of data, has proved to be not very precise and could notdetermine risks in real time. Some of the problems of the known scoringsystem are that they are not flexible, they cannot take into accounthistorical data, they are limited in the type of information which istaken into account and they have limited reporting possibility.

It is an objective of the present invention to provide an electronicdata processing system and a method of using an electronic dataprocessing system for automatically determining a risk indicator valuewhich is capable of processing a large number of current and historicalrisk parameters in real time for fast, efficient and reliabledetermination of a risk indicator value involved with performing atransaction between a client and a transaction provider.

This objective is solved by providing a system according to claim 1 anda method according to claim 10.

The present invention provides a system and a method capable ofdetermining the risk involved with a transaction on the basis of a largenumber of parameters in the shortest possible time. In particular, it isenvisaged to take into account a large number of individual parameters,e.g. typically some 25 to 50 parameters per client and account over atime span of for example 12 to 36 months, i.e. up to 1800 individualparameter values per client which may comprise such data as personalclient data, historical account data and/or historical credit data, todetermine a risk indicator value involved with performing a transactionfor said particular client in the shortest possible time. With thesystem and the method of the present invention it is possible todetermine risk indicator values overnight for a large number of clients,e.g. in the order of two millions, which means that in the order of1·10⁹ individual values have to be calculated.

For explaining the concept of the present invention, first themethodology for determining the risk indicator value is explained ingeneral, then the technical realisation is explained.

The system and method according to the present invention obtain a riskindicator value which is suitable for the identification of high-riskclients and enables an efficient discrimination between high-risk andlow-risk clients. For example the method can be applied for the riskassessment of private and corporate clients. The methodology of the riskindicator value is not limited to any specific application but can beapplied to any type of financial and commercial transaction as well astechnical processes and other applications. The terms “client” and“transaction provider” shall be understood in a broad sense, comprisingany entities involved in requesting or initiating and granting orcompleting a specific transaction or process in which such client andtransaction provider participate.

The risk indicator value can be determined not only on a client basisbut also for a group of clients or for a local area in which suchclients are active.

The risk indicator value is determined on the basis of risk parametervalues which are available at a certain date and time as well as pastrisk parameter values to predict the development of a certaintransaction or process for the future as precisely and early aspossible.

In the following, the invention is described with reference to apreferred embodiment, by way of example, in view of the attacheddrawings.

FIG. 1 shows a schematic diagram of the methodology for determining arisk indicator value according to the present invention;

FIG. 2 shows a schematic diagram of a sub-processing stage of themethodology of FIG. 1, used for determining a risk probability based ondifferent risk parameters;

FIG. 3 shows a schematic diagram of the process of the present inventionfor determining a partial risk probability of a risk category, based ona number of risk parameters;

FIG. 3 a shows a graph of the evaluation function used in the processaccording to claim 3;

FIG. 3 b shows a scale for evaluating risk parameters;

FIG. 3 c shows a graph of the risk density of one risk parameter used inthe process according to FIG. 3;

FIG. 4 shows a schematic diagram of determining a risk indicator valuebased on partial risk probabilities of a number of risk categoriesaccording to the present invention;

FIG. 5 shows a schematic diagram of the technical architecture forrealising the process according to the present invention;

FIGS. 6 a and 6 b together show a flow chart description of the processaccording to the present invention;

FIG. 7 shows a flow chart of a process for aggregating a number of riskindicator values according to the present invention; and

FIG. 8 shows a schematic illustration of the organisation and managementof input data in a first data base means according to the presentinvention.

Before describing the details of determining a risk indicator valueaccording to the present invention, a short overview of the basic stepsfor such determination is given.

When illustrating the present invention, an embodiment is described inwhich a client is utilising a credit or loan from a bank and the riskinvolved with the credit or loan shall be evaluated, checking thevarious parameters associated with the client on a per account level.This embodiment serves for illustration purpose only, and the presentinvention is not limited to such applications. The invention may, forexample, also be applied to other financial and commercial transactions,such as internal rating system of borrowers, or other technical process,such as evaluation of key performance indicators for processsupervision.

The process for determining the risk indicator value has the basic formof:Credit risk indicator (CRI)=(Risk probability)·(net utilization)

In the context of the present embodiment, the expressions “credit riskindicator” and “risk indicator value” are used synonymously. The moregeneric term “risk indicator value” refers to a risk involved with anytype of transaction or process, wherein the term “credit risk indicator”refers to a risk indicator value describing the risk involved with acredit transaction.

For one client and one account various risk parameters are grouped intoseveral risk categories, such as Accounting, Credit History, Economicalsituation/balance sheet data, and Personal situation/managementinformation. For each risk category, a number of risk parameters isdefined and evaluated, using an evaluation function F.

The risk parameters in each risk category are classified; this meansthat using the evaluation function each risk parameter is assigned anevaluation value, e.g. in the range from 0 to 10. Subsequently, a riskparameter is evaluated as being relevant if it lies within certainranges of the interval [0; 10], e.g. in the intervals [0; 4] and [5;10]. A risk parameter is categorized as critical when evaluated in[5,10], uncritical when evaluated in [0,4] or neutral when evaluated in(4,5) Critical and uncritical risk parameters after evaluation form setof relevant risk parameters and the combination of these relevant riskparameters represents risk criteria.

Subsequently, each risk parameter which is considered to be relevant isassigned a risk density function D_(j) (jε{l, . . . , k}) fordetermining a risk density of the respective relevant parameter. Acommon risk density of all relevant parameters of one risk category isdetermined.$D:=\frac{\min\left( {D_{1},\ldots\quad,D_{k}} \right)}{\int_{\lbrack{0,1}\rbrack}{\min\left( {D_{1},\ldots\quad,D_{k}} \right)}}$

From this common risk density a partial risk probability P_(i) of therespective risk category is derived. P_(i) := ∫_([0, 1])tD(t)  𝕕t.

To each partial risk probability a weight W_(i) is associated whereinthis weight depends on two factors: the first factor is based on thenumber of critical parameters within a category, and the second factorhas been determined empirically.

The total risk probability P of all risk categories is determined as aweighted average value of the partial risk probabilities.P:=WA(P ₁ ,W ₁ , . . . , P _(k) ,W _(k)).whereinW _(i)=(j+number of critical parameters in the risk category i)×(definedweight of the respective risk category i)

Further, a net utilization of a client is determined as:net utilization=(utilization at a point in time)−(value of collateralsat a point in time).

In the following, the method will be described in further detail.

In the methodology of the present invention for determining the riskindicator value (or credit risk indicator, CRI) the complex relationshipbetween individual risk parameters has been taken into account to enableprecise risk identification. Risk parameters within one risk categoryare all treated equally.

FIG. 1 shows the overall design of the methodology of the presentinvention in which the risk indicator value (CRI) is determined as aproduct of the total risk probability of one transaction (per client andper account) and the net utilisation associated with said transactionand client.

For determining the total risk probability, parameters with respect tothe accounting, credit history, economical situation, and personalsituation of the client are taken into account. For determining the netutilisation involved with the transaction in question, the amount ofmoney claimed as well as the value of collaterals at a certain point oftime provided by the client are taken into account.

The risk indicator value automatically indicates the risk associatedwith a particular client at a particular point in time and is defined asthe product of risk probability and net utilization. The riskprobability designates the probability with which the respectivetransaction poses a risk for the transaction provider.

The client input data are grouped in the four risk categories shown inFIGS. 1 and 2. Within each category, risk parameters are defined whichare derived from client input data. The combination of risk parameterswithin each category are processed to determine a partial riskprobability of said category. Each category is assigned a weight W_(i).

FIG. 2 shows a schematic diagram of the first and the second processingstage for determining the risk probability from a number of riskparameters, grouped according to four risk categories. In the firststage of the process according to the present invention, risk criteriaare formed based on relevant risk parameters and partial riskprobabilities are determined and associated with respective weights ofthe risk categories. In the second processing stage, the partial riskprobabilities and associated weights of all risk categories are combinedto determine the risk probability. Accordingly, the risk parameters forthe first stage of the methodology according to the present inventionare measured, e.g. in terms of amount, frequency, variability etc., anddynamically evaluated using associated evaluation functions. To obtain ahighly reliable system for risk identification it is important that therisk parameters and evaluation criteria are as complete as possible.Evaluation takes place at a certain day and time on the basis of allavailable up to date information with respect to the above mentionedcategories.

Risk parameters x are values which contain risk relevant information ofthe transaction. They directly reflect actual client input data. Theirrelevance therefore essentially depends from the quality of theavailable data. For obtaining a reliable result it is important to takeinto account as much information as possible. Nevertheless, for the sakeof describing the present invention we shall illustrate only a fewexamples of the large number of possible risk parameters which can betaken into'account in the methodology of the present invention. In onepreferred embodiment of the present invention it is e.g. envisaged totake into account up to fifty risk parameters.

Examples for Risk Parameters Taken into Account in the PresentInvention.

Risk Category: Accounting

1. Turnover with respect to tendency and absolute values:

-   -   The relative changes of the accumulated annual turnover is        determined with respect to the turnover of the preceding years        based on e.g. 36 months. The relative change of the accumulated        turnover of one month is determined with respect to the        preceding month, e.g. on the basis of the last four months.        Further, the accumulated annual turnover of e.g. the past three        years and the monthly turnover of the last year are determined.

2. Long-term debtor

-   -   Permanent significant utilization of credit line of a        turnover-relevant account during the past six months

Risk Category: Credit History

1. Seizure: Direct data are available with respect to the date andamount of seizures as well as the number of seizures during the last 12months.

2. Reminders: Data are directly available with respect to dates, amountsand number of reminder letters e.g. during the last 12 months.

3. Expired credit line: time since the expiration of credit line

Risk Category: Economical Situation:

1. Property: The actual value of total property of a client

2. Income: The total income of a client

Category: Personal Situation:

1. Age

2. Profession

Please note that the above are only few examples of a large number ofpossible risk parameters to be taken into account.

According to the present invention, once the risk parameters to be takeninto account are defined and the necessary client input data have beenobtained, risk parameter values x are determined and evaluated usingrisk parameters specific evaluation functions F(x). This isschematically shown in the first three stages of FIG. 3, designated riskcategory, measure value of risk parameter, and assess evaluation valueof risk parameter.

The evaluation functions are defined specifically for each riskparameter, based on a generic equation. The evaluation functions assignsvalue in the interval of [0,10] to each risk parameter. The evaluationfunction serves for distinguishing three ranges, i.e. uncritical,neutral and critical. In the ranges uncritical and critical, which aredeemed to be risk relevant ranges, the evaluation function ismonotonously increasing to facilitate the evaluation and discriminationof relevant features. In the following, one example for an evaluationfunction is given without the invention being limited to this specificfunction. The evaluation function is defined by threshold values a, band stretching parameters c, d. The threshold values a and b determinethe thresholds between the uncritical and neutral, and neutral andcritical ranges respectively. The neutral range lies between theuncritical and critical range. The stretching parameters c, d determinethe degree of escalation of the evaluation function. The larger thestretching parameters are, the larger is the escalation of therespective risk parameter. a, b, c, and d are constants for a respectiverisk parameter. The evaluation function is designated F_(a,b,c,d):${F_{a,b,c,d}(x)}:=\left\{ {\begin{matrix}{{4{\exp\left( {c\left( {x - a} \right)} \right)}},} & {x \leq a} \\{{\frac{x - a}{b - a} + 4},} & {a < x < b} \\{{5\left\lbrack {2 - {\exp\left( {- {d\left( {x - b} \right)}} \right)}} \right\rbrack},} & {b \leq x}\end{matrix}.} \right.$

FIG. 3 a shows a graphical representation of the evaluation function.

At the right hand side of the evaluation function the three rangesuncritical, neutral and critical are indicated.

For certain parameters the evaluation function may also be monotonouslydecreasing. Then it has the form F_(b,a,d,c)(−x). It can also happenthat only parts of the function are relevant for the evaluation. Thevalues a, b, c, d satisfy the following:

The threshold between the neutral and the critical range is alwaysdefined;

the threshold between the uncritical and the neutral range may beomitted;

the stretching factor for the critical range is always defined;

the stretching factor for the uncritical range can be omitted

For determining the values a, b, c, d for each risk parameter empiricalknowledge has been used.

For example: for parameter i, a and b are the threshold values. Further,this parameter has the evaluation value y₁ for the parameter valuex₁(x₁<a) and y₂ for x₂(b<x₂). The stretching parameters are thendescribed by:$c = {{{- \frac{\ln\left( {y_{1}/4} \right)}{x_{1} - a}}\quad{and}\quad d} = {- {\frac{\ln\left( {\left( {10 - y_{2}} \right)/5} \right)}{x_{2} - b}.}}}$

A detailed description of the evaluation functions is given by way ofexample for a selected number of risk parameters below.

It can be necessary to evaluate risk parameters several times. If a riskparameter does not show a tendency, it will be designated to be neutrale.g. F=4, 5.

Examples for Evaluating Specific Risk Parameters:

Risk Category: Accounting

1. Turnover:

AU_(i) is defined as the average utilization during the month i.

1.1) Tendency

-   -   Measurement: relative change of the accumulated annual turnover        as compared to the turnover of the preceding year, based on the        last 36 months    -   A T_(i): accumulated annual turnover during the last 12 months        (months (12(i−1)+1) to 12i)   i.)  If  AT⁻² > AT⁻¹ > AT₀, then        $\quad{{x:={{\max{\left\{ {{\frac{{AT}_{- 1}}{{AT}_{- 2}} - 1},{\frac{{AT}_{0}}{{AT}_{- 1}} - 1}} \right\}.\quad\text{ii.)~~If~~0}}} \neq {AT}_{- 2} < {AT}_{- 1} < {AT}_{0}}},\text{then~~}}$        $\quad{{x:={{\min{\left\{ {{\frac{{AT}_{- 1}}{{AT}_{- 2}} - 1},{\frac{{AT}_{0}}{{AT}_{- 1}} - 1}} \right\}.\text{iii.)~~If~~0}}} = {{AT}_{- 2} < {AT}_{- 1} < {AT}_{0}}}},{{\text{then~~}x}:={\frac{{AT}_{0}}{{AT}_{- 1}} - 1.}}}$    -   evaluation function:        -   PC (Private Client)        -   (−a)=0.1 threshold for significant decrease of turnover        -   (−b)=0.2 threshold for significant increase of turnover            (a>b) $c = {- \frac{\ln\quad 0.4}{0.15}}$            $d = {- \frac{\ln\quad 0.25}{0.1}}$        -   CC (Corporate Client)        -   (−a)=−0.1 threshold for significnat decrease of turnover        -   (−b)=0.1 threshold for significant increase of turnover            (a>b) $c = {- \frac{{\ln\quad 0},4}{0,15}}$            $d = {- \frac{{\ln\quad 0},01}{0,9}}$            F_(Annual  change  of  turnover) = F_(b, a, d, c)(−x)

If none of the cases i, ii, iii applies, the parameter is neutral:F_(Annual change of turner)=4.5

-   -   Measurement: Relative change of accumulated monthly turnover as        compared to accumulated turnover of preceding month, based on        last four months    -   MT_(i): accumulated monthly turnover for month i          i.)  If  MT⁻³ > MT⁻² > MT⁻¹ > MT₀, then        $\quad{{x:={{\max{\left\{ {{\frac{{MT}_{- 2}}{{MT}_{- 3}} - 1},{\frac{{MT}_{- 1}}{{MT}_{- 2}} - 1},{\frac{{MT}_{0}}{{MT}_{- 1}} - 1}} \right\}.\quad\text{ii.)~~If~~0}}} \neq {MT}_{- 3} < {MT}_{- 2} < {MT}_{- 1} < {MT}_{0}}},{then}}$        ${{\text{~~}\quad x}:={{\min{\left\{ {{\frac{{MT}_{- 2}}{{MT}_{- 3}} - 1},{\frac{{MT}_{- 1}}{{MT}_{- 2}} - 1},{\frac{{MT}_{0}}{{MT}_{- 1}} - 1}} \right\}.\text{iii.)~~If~~0}}} = {{MT}_{- 3} < {MT}_{- 2} < {MT}_{- 1} < {MT}_{0}}}},{then}$        $\quad{x:={\min{\left\{ {{\frac{{MT}_{- 1}}{{MT}_{- 2}} - 1},{\frac{{MT}_{0}}{{MT}_{- 1}} - 1}} \right\}.}}}$    -   evaluation function:        -   PC        -   (−a)=−0.0083 threshold for significant decrease of turnover        -   (−b)=0.016 threshold for significant increase of turnover            (a>b) $c = {- \frac{\ln\quad 0.4}{0.0077}}$            $d = {- \frac{\ln\quad 0.5}{0.008}}$        -   CC        -   (−a)=0,0083 threshold for significant decrease of turnover        -   (−b)=0,0083 threshold for significant increase of turnover            (a>b) $c = {- \frac{{\ln\quad 0},4}{0,0117}}$            $d = {- \frac{{\ln\quad 0},25}{0,0917}}$            F_(Monthly  change  of  turnover) = F_(b, a, d, c)(−x)

If none of the cases i, ii, iii applies, the parameter is neutral:F_(Monthlychangeofturnover)=4.5

1.2) Amounts

Measurements: If A U₀>0, Accumulated annual and monthly turnover

-   -   No turnover during last year

If AT₀=0, the account has no turnover. Evaluation: PC: 10, CC: 10 (PCPrivate Client; CC=Corporate Client)

-   -   No turnover during last two months    -   If MT⁻¹=MT₀0, the account shows no turnover. Evaluation: PC: 10,        CC:10    -   Accumulated annual turnover during the last year with regard to        the average limit    -   AL_(i): average annual limit for the months (12(i−1)+1) to 12i        (arithmetic mean value of the monthly limits of year i)    -   If AL₀>0, then ${x:=\frac{{AT}_{0}}{{AL}_{0}}},$    -    relative accumulated yearly turnover during the last year        Evaluation  Function: PC/CC(−a) = 3 (−b) = 12        $c = {- \frac{\ln\quad 0.4}{0.6}}$        $d = {- \frac{\ln\quad 0.5}{12}}$        F_(Annual  .rel  .turnover) = F_(b, a, d, c)(−x)    -   Accumulated monthly turnover during the last month with regard        to the average limit    -   L_(i): average monthly limit during month i    -   If L₀>0, then ${x:=\frac{{MT}_{0}}{L_{0}}},$    -    relative accumulated monthly turnover during last month        Evaluation  function: PC/CC(−a) = 0.33 (−b) = 1        $c = {- \frac{\ln\quad 0.4}{0.133}}$ d = −ln   0.5        F_(Monthly  .rel  .turnover) = F_(b, a, d, c)(−x)        2. Long-Term Debtor

Measurement: relative utilization of a credit lines of aturnover-relevant account during the past six months

A U_(i): average utilization during the month i

L_(i): average limit during the month i

If for the past 6 months UL_(i)>0 then${{RAU}_{i}:=\frac{{AU}_{i}}{L_{i}}},$the relative utilization for the month ix:=min{RAU⁻⁵,RAU⁻⁴,RAU⁻³,RAU⁻²,RAU⁻¹,RAU₀}Evaluation function:

PC

a=−0.01

b=0.65 Threshold for critical relative utilization

c not relevant, since x≧0 $d = {- \frac{\ln\quad 0.2}{0.2}}$

CC

a=−0.01

b=0.65 Threshold for critical average utilization

c not relevant, since x≧0 $d = {- \frac{\ln\quad 0.2}{0.35}}$F_(Long-term  .debitor) = F_(a, b, c, d)(x)Risk Category: Credit History3. Expired Credit Line

Measurement: months since the credit line has expired.

If a credit line has expired, x: months since expiration

Evaluation function: PC/CC

-   -   a=−1    -   b=3 threshold for critical duration    -   c not relevant, since x≧0 $d = {- \frac{\ln\quad 0.01}{3}}$        F_(Expire  .credit  .line) = F_(a, b, c, d)(x)        Risk Category: Personal Situation        4. Age        Age of Client (only for PC)        Measurement: Data are available directly        x: Age in years        Evaluation function:

PC

a=30

b=50

c not relevant $d_{1} = {- \frac{\ln\quad 0.4}{10}}$$d_{2} = {- \frac{\ln\quad 0.6}{10}}$  F _(Age) =F _(−b,−a,c,d) ₁ (x),if x≧40F _(Age) =F _(a,b,c,d) ₂ (x), if x≦405. Profession:Current profession of client (PC)Measurement: Data are available directly

-   -   State employee: 4.5    -   Employee: 5.2    -   Pensioner 5.5    -   Self-employed: 6    -   Unemployed: 7

Above are a few examples for illustrating how the constants of the riskevaluation function F can be determined with respect to each riskparameter.

Below, examples for risk parameters x, threshold values a, b, andstretching parameters c, dare summarised in a table for the aboveexamples, separated according to private clients (PC) and corporateclients (CC). Threshold values Stretching parameters Risk parameter a bc d Account management (PC/CC) 1. Turnover i.) Tendency Annual turnover:PC 0.1 −0.2 6.1086 13.8629 CC 0.1 −0.1 6.1086 5.1169 Monthly turnover PC0.0083 −0.016 118.9988 86.6434 CC 0.0083 −0.0083 78.3154 15.1177 ii.)Amounts (PC/CC) AT₀ = 0 MT⁻¹ = MT₀ = 0 Relative −3 −12 1.5272 0.0578accumulated yearly turnover Relative −0.33 −1 6.8894 0.6931 accumulatedmonthly turnover 2. Long-term debtor PC −0.01 0.65 — 8.0472 CC −0.010.65 — 4.5984 Credit history/debts (PC) 3. Expired credit line PC −1 3 —1.5351 CC −1 3 — 1.5351 Personal Situation (PC) 4. Age 30 50 — 0.0916(d₁) — 0.0511 (d₂) 5. Profession

FIG. 3 b schematically shows the result of applying the evaluationfunction F to each risk parameter value x. The result is a number in aninterval from zero to ten (i.e., [0, 10]). U is designating anuncritical area and C is designating a critical area. The interval [4,5] designates a neutral area wherein the parameter is not relevant fordetermining an overall risk probability. This means that risk parameterswhich are outside of the neutral range N=[4, 5] are designated riskrelevant parameters or non-neutral parameters.

According to the present invention, as also illustrated in FIG. 3, oncethe evaluation value or relevance of each risk parameter is determined,the non-neutral risk parameters for each risk category are processedfurther to determine a risk-probability of each risk category; orpartial risk probability. For determining the risk probability, eachnon-neutral risk parameters is assigned a risk density D_(j) which isdefined as a function in the interval [0, 1] so that:D_(j):  [0, 1] → R_( > 0)  with  ∫_([0, 1])D_(j) = 1.

This is also shown in FIG. 3.

Each risk density function is again defined for each non-neutral riskparameter separately, as explained below.

The risk density function serves for determining a risk distribution foreach individual risk parameter. The risk density function is determinedfor the non-neutral risk parameters. To obtain a partial riskprobability of each respective category, the risk densities of allrelevant risk parameters are aggregated within one category according tothe following equation, within one category$D_{{Cat}.i}:=\frac{\min\left( {D_{1},\ldots\quad,D_{k}} \right)}{\int_{\lbrack{0,1}\rbrack}{\min\left( {D_{1},\ldots\quad,D_{k}} \right)}}$

The result again is a risk density—the common risk density of allrelevant parameters of one category, normalised to obtain a value in theinterval [0, 1]. If in one category no risk parameter is relevant, aneutral risk density D is assigned.

The partial risk probability P_(i) for the respective category i is arisk to be expected and is calculated according to the equation:P_(i) := ∫_([0, 1])t ⋅ D_(Cat.i)(t)𝕕t.

This partial risk probability is the result of the first processingstage of the method according to the present invention shown in FIG. 3.

As outlined above, a risk density function D is assigned to eachrelevant risk parameter wherein the risk density function represents thedensity of the risk probability of the respective risk parameter, takinginto account its parameter value. The general form of the risk densityfunction is explained below. It is defined using risk density parameterse, f and g.

The risk density parameters e, f, g are dependent on the specific riskparameter and its evaluation, as detailed below:

e defines the maximum of D (e=0,8 in FIG. 3 c) over R, where R are thereal numbers

f defines the degree of escalation for x<e; and

g defines the degree of escalation for x>e.

It is assumed that eεR, f, g>0, wherein R is the set of real numbers.The pre-risk density of a single risk parameter D′_(e,f,g): R→R_(>0) isthen defined as: ${D_{e,f,g}^{\prime}(x)}:=\left\{ {\begin{matrix}{{\exp\left( {- {f\left( {x - e} \right)}^{2}} \right)},{x \leq e}} \\{{\exp\left( {- {g\left( {x - e} \right)}^{2}} \right)},{e \leq x}}\end{matrix}.} \right.$

FIG. 3 c shows a graphical representation of the risk density functionaccording to the above equation.

To obtain a risk density D_(e,f,g) over the interval [0,1] for eachrespective risk parameter, the following equation is used:$D_{e,f,g}:={\frac{D_{e,f,{g{\lbrack{0,1}\rbrack}}}^{\prime}}{\int_{\lbrack{0,1}\rbrack}D_{e,f,g}^{\prime}}:\left. \left\lbrack {0,1} \right\rbrack\rightarrow{R_{> 0}.} \right.}$

The risk density function according to the present invention is ageneralisation of the density function of normal distribution. Nosymmetry was assumed: Therefore it is possible to describe asymmetricdistributions. Such probability densities can be found in real lifeprocesses.

In the following, the determination of the risk density function of someexemplary risk parameters is explained. The parameters e, f, g depend onthe respective risk parameters and their evaluation values. Whendetermining e, f, g statistical analysis and expert knowledge has beenused.

The parameters e, i.e. the maximum of D when extended over R, isdetermined directly: At this point the risk density is the highest. Forfurther description of the risk density function, further referencepoints (x₁, y₁) and (x₂, y₂) are used, wherein x₁<e<x₂ and 0<y₁, y₂≦1.This reference points are chosen so that the relative risk frequency iswithin the confidence interval [x₁, x₂].

For these constants, one obtains:$f = {{{- \frac{\ln\quad y_{1}}{\left( {x_{1} - e} \right)^{2}}}\quad{and}\quad g} = {- {\frac{\ln\quad y_{2}}{\left( {x_{2} - e} \right)^{2}}.}}}$

This defines the pre-risk density function D′_(e,f,g) for a single riskparameter. Normalising leads to D_(e,f,g) as explained above.

For the risk density parameters e, f, g, the following should beobserved:

the parameter e, defining the maximum of D when extended over R, isalways defined;

the risk density parameter f may be missing;

the risk density parameter g may be missing.

The following example shall help to illustrate the process of obtainingthe parameters for calculating the risk density function:

The risk density function of a debitor, having a permanent relativeutilisation of 65 percent during the last six months is looked for:

-   -   The portfolio of engagements, regarding the risk parameter “long        term debitor 65%” is investigated;    -   identifying the highest risk concentration at 0,4 (e=0,4)    -   the number of risk relevant engagements most probably is between        25% and 55%, so that x₁=0,25 and x₂=0,55;    -   to correctly describe the risk concentration, the probability        outside of [x₁, x₂] must be sufficiently low.    -   Therefore, the following estimation is obtained: y₁,        y₂<0,1(x₂−x₁);    -   the pairs (0,25; 0,01) and (0,55; 0,01) fulfil these conditions,        so that the following parameters f, g are derived:        $f = {{{- \frac{\ln\quad 0.01}{(0.15)^{2}}}\quad{and}\quad g} = {- {\frac{\ln\quad 0.01}{(0.15)^{2}}.}}}$

A detailed description of the risk density for some exemplary riskparameters follows:

Risk Category: Accounting

1. Turnover

1.1 Tendency •  F = F_(Annual  .change  .of  .turnover) If  F ≤ 4:    e(F) = 0   f(F)  not  relevant$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{(0.01)^{2}}}}$ If  F ≥ 5:  e(F) = 0.667F − 0.1333$\quad{{f(F)} = {- \frac{\ln\quad 0.01}{(0.1)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0167F} + 0.0666} \right)^{2}}}}$•  F = F_(Monthly  change  .of  .turnover) If  F ≤ 4:     e(F) = 0  f(F)    not  relevant$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{(0.01)^{2}}}}$   If  F ≥ 5:    e(F) = 0.0667F − 0.1333$\quad{{f(F)} = {- \frac{\ln\quad 0.01}{(0.1)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0167F} + 0.0666} \right)^{2}}}}$1.2 Amounts •AT₀ = 0:     e = 0.9$\quad{f = {- \frac{\ln\quad 0.01}{(0.15)^{2}}}}$$\quad{g = {- \frac{\ln\quad 0.01}{(0.6)^{2}}}}$ •MT⁻¹ = MT₀ = 0:  e = 0.25 $\quad{f = {- \frac{\ln\quad 0.005}{(0.05)^{2}}}}$$\quad{e = {- \frac{\ln\quad 0.005}{(0.05)^{2}}}}$•  F = F_(Annual  .rel  .turnover) If  F ≤ 4:   e(F) = 0.00125F$\quad{{f(F)} = {- \frac{\ln\quad 0.001}{\left( {{0.00125\text{F}} + 0.05} \right)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.0001}{\left( {{0.001\text{F}} + 0.001} \right)^{2}}}}$If  F ≥ 5:   e(F) = 0.1167F − 0.2833$\quad{{f(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0167\text{F}} - 0.1867} \right)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0167\text{F}} + 0.1833} \right)^{2}}}}$•  F = F_(Monthly  .rel  .turnover) If  F ≤ 4:   e(F) = 0.00125F$\quad{{f(F)} = {- \frac{\ln\quad 0.0001}{\left( {{0.00125\text{F}} + 0.05} \right)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.001}{\left( {{0.001\text{F}} + 0.001} \right)^{2}}}}$If  F ≥ 5:     e(F) = 0.1167F − 0.2833$\quad{{f(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0167\text{F}} - 0.1867} \right)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0167\text{F}} + 0.1833} \right)^{2}}}}$2. Long-Term DebtorF=F_(Long-term,debitor)PC (Private Client): If  F ≥ 5:     e(F) = 0.08757F − 0.0375$\quad{{f(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0125\text{F}} + 0.0875} \right)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{(0.15)^{2}}}}$CC (Corporate Client): If  F ≥ 5:     e(F) = 0.1F − 0.1$\quad{{f(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0125\text{F}} - 0.2125} \right)^{2}}}}$$\quad{{g(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.0125\text{F}} + 0.0875} \right)^{2}}}}$Risk Category: Credit History3. Expired Credit Line F = F_(Expired.credit.line.) Falls  F ≥ 5:  $\begin{matrix}{{e(F)} = {{0.1F} - 0.2}} \\{{f(F)} = {- \frac{\ln\quad 0.01}{\left( {0.03F} \right)^{2}}}} \\{{g(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.01F} - 0.3} \right)^{2}}}}\end{matrix}$Category: Personal Situation4. Age F = F_(Age) If  F ≥ 5: $\begin{matrix}{{e(F)} = {{0.0833F} - 0.1667}} \\{{f(F)} = {- \frac{\ln\quad 0.01}{\left( {{0.05F} - 0.1} \right)^{2}}}} \\{{g(F)} = {- \frac{\ln\quad 0.01}{(0.15)^{2}}}}\end{matrix}$5. Profession employee: (F = 5.2) $\begin{matrix}{e = 0.02} \\{f = {- \frac{\ln\quad 0.01}{(0.015)^{2}}}} \\{g = {- \frac{\ln\quad 0.01}{(0.015)^{2}}}}\end{matrix}$ pensioner: (F = 5.5) $\begin{matrix}{e = 0.05} \\{f = {- \frac{\ln\quad 0.01}{(0.03)^{2}}}} \\{g = {- \frac{\ln\quad 0.01}{(0.15)^{2}}}}\end{matrix}$ self-employed: (F = 6) $\begin{matrix}{e = 0.2} \\{f = {- \frac{\ln\quad 0.01}{(0.1)^{2}}}} \\{g = {- \frac{\ln\quad 0.01}{(0.3)^{2}}}}\end{matrix}$ unemployed: (F = 7) $\begin{matrix}{e = 0.5} \\{f = {- \frac{\ln\quad 0.01}{(0.2)^{2}}}} \\{g = {- \frac{\ln\quad 0.01}{(0.3)^{2}}}}\end{matrix}$

The above are only a few examples of determining the risk density forselected risk parameters for determining the risk probability for eachindividual risk parameter, as shown in FIG. 3 of the drawings.

As explained above, once the risk density function is determined foreach relevant risk parameter and, accordingly, the associated riskprobability of said parameter is determined, said risk probabilities areaggregated to determine a partial risk probability of the respectiverisk category according to the equation:P_(i): = ∫_([0, 1])  t ⋅ D_(Cat.i)(t)  𝕕t.

As shown in FIG. 4, for each of the risk categories, such as accounting,credit history, economical situation and personal situation, a partialrisk probability is calculated. Further, each partial risk probabilityis assigned a weight which is defined as:W _(i)=(1+number of critical parameters in the risk categoryi)×(predefined evaluation of the weight for the risk category i)

The weight has a process dependent (the first factor) and a processindependent (the second factor) component.

In the process dependent component the critical parameters (parametershaving an evaluation value≧5) of each category are accounted for.

In the process independent part each risk category is evaluated based onempirical observation. For example the risk category Accounting is moreimportant than Personal Situation. Furthermore, the quality andcompleteness of available client input data may be taken into accountwhen determining the weights. Even further, the weights can be adaptedto time intervals from which the client input data are derived.

Accordingly, each of the risk categories is evaluated by a pair ofpartial risk probability and weight (P_(i), W_(i)) with W_(i)>0. Thesepairs are aggregated to form a weighted average value, the riskprobability P for a certain client provider transaction:$P = {{{{WA}\left( {P_{1},W_{1},\ldots\quad,P_{n + 1},W_{n + 1}} \right)}\text{:}} = {\frac{\sum\limits_{i = 1}^{n + 1}\quad{W_{i}P_{i}}}{\sum\limits_{i = 1}^{n + 1}\quad W_{i}}.}}$

The risk probability P expressed as the weighted average is thanmultiplied with the net utilization:Net utilization=(utilization at a point in time)−(value of collateralsat a point in time)to determine the credit risk indicator (CRI) or risk indicator value.This credit risk indicator or risk indicator value is expressed in anamount of money referring to the risk involved with a certaintransaction.

FIG. 5 shows a schematic diagram of a technical architecture forrealising method and computer system according to the present invention.

FIG. 5 shows a source system 10 for providing client input data e.g.from a client data base resident in a bank or another transactionprovider. Client input data are read by the transformation module 12,comprising an optional filter 14, a processing unit 16 for collectingand processing historical client input data and the first and seconddata bases 18, 20 for storing client input data and threshold values andstretching parameters, respectively. The transformation module 12comprises an input 22 for reading client input data and an input 24 forreading threshold values a, b and stretching parameters c, d used forsubsequently determining the evaluation values of risk parameters.

The data from the transformation module 12 are read into a processingmodule 26, comprising first and second calculating units 28, 30, anoptional debug & verify unit 32 and an optional verification & backtesting information data base 34. The results of the first calculationunit 28 are stored in a memory and transferred to the second calculationunit 30. The results of the second calculation unit 30 are written intoa result data base 36.

In general, the data base module 12 and the processing module 26 can beimplemented with any suitable, known data base structures and processingunits. In one preferred embodiment, the core of the processing module 26has been developed in C, on a hardware platform, interacting with arelational database. For processing the database, among a number ofpossibilities, such as ODBC (Open Database Connectivity) and nativeaccess, in the preferred embodiment ODBC has been selected as aninterface between the code and the database. The second database 20 forthe threshold values a, b and stretching parameters c, d, required bythe C code processing module 26 has been generated. The weights for thefour different risk categories have been moved into an optional externalfile.

Calculation unit 28 is used for calculating the risk parameters x, theevaluation functions F(x) and the risk density parameters e, f, g usedfor determining the respective density functions. Calculation unit 30 isused for calculating the partial risk probabilities of the differentrisk categories, the overall risk probability as a function of thepartial risk probabilities, the net utilization and the risk indicatorvalue. Both calculation units 28 and 30 may operate in parallel.Optionally and also in parallel, results from the first calculation unit28 can be written into debug information unit for debugging and testingthe system.

For calculating the value of risk parameters, risk evaluation functionsand density functions, in a preferred embodiment, the original code wasimplemented in such a way that for each risk parameter a separatefunction is written, which is then called by a function pointer in themain calculation loop.

With regard to the implementation of the first and second data base 18,20, in the preferred embodiment, any relational database scheme can beused.

The number of the fields accessed by the code is kept minimal so that nofields other than those required by and involved in the calculation areaccessed by the code. This feature provides not only a reduction in thedatabase access overhead, but also a higher degree of compatibility topossible further modifications that may be done on the databasestructure.

The calculation module uses the same key identifier fields andrelationships between the tables as those defined in the OperationalData Store (ODS). Therefore the possibility of clashes between thecalculation module and the other elements that are accessing thedatabase is minimised.

The program is designed to be executed periodically (i.e. on a daily,weekly or monthly basis, according to the needs) to update the CRIparameters in the database. This method has some advantages such as:

No user interaction is required, so that it is suitable for batchprocessing

The up to date data of any client can be acquired any time.

The drawback is that, it introduces significant overhead to the system,as every single client data has to be calculated regardless of if it isneeded to be calculated or not. To overcome this drawback, a possiblesolution would be introducing a field in the database to indicate whichclient data have been changed and are needed to be calculated again sothat only those need can be calculated.

The CRI Calculation Module exists as a standalone executable file. Themodule interacts with an interface to read in the data required for thecomputation and then the output is again written back to the database.The program flow is as follows:

The program reads in the threshold values a, b and stretching parametersc, d table from a database table.

From the Operational Datastore (ODS), it reads in client data whichconsists of:

-   -   Personal Information    -   Account Information    -   Subaccount Information    -   Credit Information

It makes the calculations for each account and aggregates the results toclient level

Then it writes the results into the Star Schema and optionally somedebugging information onto the screen, so that more internal parameterscan be examined.

Star Schema is a relational database schema for representingmultidimensional data. A star schema is a set of tables comprising asingle, central fact table surrounded by de-normalized dimensions. Eachdimension is represented in a single table. Star Schema implementsdimensional data structure with de-normalized dimensions.

FIG. 8 shows a particular advantageous way of orgarising the first database 18 containing the current and historical client input data.

The computation of the credit risk indicator (CRI) requires data for nmonths. Reading directly from the CDS (client data source) 10 would meanto read data from x sources for n months. For efficient calculation ofthe CRI the data is collected with an ETL(Extract-Transformation-Load)—Tool and put into an optimized table forthe C-program.

The historization concept comprises two steps:

Step one moves the values of the actual month to the next month.

Step two fills the Month 0 with the values of the actual month. Theadvantage is to read only one month of data from the client data source.

So the month 0 always contains the current values. With this concept itis possible to access the historizized data directly. For example: Toget the values of the last three months, just access Month 0-2.

By applying the above described scheme for organising in particular theclient input data base 18, it is possible to organise the data in a waythat all client input data necessary for calculating the credit riskindicator for one specific client are obtained by a single access tothis data base.

As mentioned above the risk indicator value is calculated typically onceper month at a fixed date, after all necessary data have been collectedbased on a monthly report. The monthly determination of the credit riskindicator values are stored in the results data base for a definednumber of months so that a risk history for each client and account canbe assembled for a period of several years.

FIGS. 6 a and 6 b show a flow diagram for performing the methodaccording to the present invention. As explained on the bottom of FIG. 6b the different designs of the boxes relate to tasks, decision boxes,input/output data and flat files for storing input or output data.

As shown in FIG. 6 a, threshold values a, b and stretching parameters c,d are read 100 from a table and stored in thresholds and stretchingparameters database 20, the threshold values a, b and stretchingparameters c, d necessary for the specific evaluation function arelooked up 101 and stored in a memory 102 internal to the processingmodule 26. In a parallel or subsequently client input data are read 103from client input data base 18, preferably by a single access to saiddatabase 18. Using the relevant client input data for a respective riskparameter and the applicable threshold values a, b and stretchingparameters c, d, the risk parameter value x and the correspondingevaluation function F(x) are calculated 104 and also written to memory105 internal to the processing module 26. From the result of theevaluation function F(x), it is determined whether the risk parameter isevaluated as relevant, i.e. critical or uncritical If no, the nextparameter is determined and evaluated 107. If yes, the risk parameter isassigned a risk density D 109. For this purpose, the necessary riskdensity parameters e, f, g are computed 108, as outlined above. Theresult, i.e. the risk density D and the risk density parameters e, f, gare stored in memory 110 internal to the processing module 26.

In the next step 111 it is determined whether there are further relevantrisk parameters in the category under examination. If no, the next riskcategory is examined 112. If yes, it is determined whether the last riskparameter in the respective category is reached 113. If no, the nextrisk parameter in the risk category is evaluated 114. If yes, it isdetermined whether the respective risk category contains relevant, i.e.non-neutral parameters 115. If no, a neutral risk density is assigned tothe risk category 116 and the partial risk probability is calculated asa neutral partial risk probability 117 and stored in memory internal tothe processing module 26. If yes, the respective risk category isassigned a risk density 118. Using this risk density, a risk expectationor partial risk probability is calculated for the respective riskcategory 119 as a normalised risk density as described above. Thepartial risk probability is stored in memory 120 internal to theprocessing module 26.

In the next step, the weight W_(i) associated with the respective riskcategory is calculated 122, using a fixed evaluation factor FE for thegiven risk category and the number of critical parameters in the givencategory as described above 121. The weight W_(i) is stored in memory123 internal to the processing unit 26.

Than it is determined whether the last risk category has been reached124. If no, the method continues with the next category 125. If yes, theoverall risk probability is computed 126 in the second calculation unit30 as an weighted average of the partial risk probabilities. The overallrisk probability is stored 127 in the result data base 36.

Subsequently, the net utilization for the respective client, account andtransaction is calculated 129, using information about the actualutilization in the transaction and value of collaterals available forthe transaction from the input data base 18; the necessary data areinput at 128. The net utilization is stored 130 in the result data base36.

Finally, the credit risk indicator is calculated 131 as the product ofthe net utilization and the overall risk probability and output 131 andstored 132 in the result data base 36.

The above described steps closely follow the methodology describedabove. Of course, variations and modifications may be realised withinthe scope of the independent claims. The expert will know how toimplement the individual steps for optimum efficiency and speed.

In one preferred embodiment of the invention it is also provided that anumber of risk indicator values may be aggregated to improvepossibilities of evaluation of the behaviours of a number of accounts ora number of clients according to different criteria. Some of thecriteria might be the overall development of client, a group of clients,a group of transaction providers, a regional area or the like. As thesize of each group may vary considerably it is necessary to provide somemeans of comparing different groups. This is possible by using the riskprobabilities according to the present invention because the method ofthe present invention provides both absolute and normalised results.

The risk probability for a group of individual transactions is given bythe weighted average of the individual risk probabilities, weighted byusing the utilization. When additionally considering the total netutilisation, i.e. the sum of these individual net utilizations, a riskevaluation of the total group is possible.

A risk indicator value may be determined for a single transaction, asingle client or a group of clients and/or transactions.

FIG. 7 shows one example of a flow diagram of determining the riskindicator value on an account level. Aggregation to higher levels andother dimensions are possible.

In step 140, a number of risk indicator values of various accounts ofone client are aggregated. In step 141 it is determined whether the lastaccount of the respective client is reached. If no, the next account isadded 142. If yes, the risk indicator value for the respective client iscalculated 143 and transferred 144 to the result data base 36, in step145.

Further modifications can be made. For example, the risk probability andthe net utilization can be evaluated separately. If for example thenet-utilisation is very high, the transaction can be looked at closereven though the risk probability itself is low or vice versa.

Further, methods for debugging and back testing the method of thepresent invention may be introduced. By back testing, the quality of theresults are verified and eventually optimised. Back testing can be usedto fine tune the threshold values a, b, stretching parameters c, d andrisk density parameters e, f and g.

The present invention as disclosed above provides a system and methodfor evaluating the risk involved with a transaction between a client anda transaction provider which is capable of automatically processing alarge amount of data in shortest time and providing a highly reliableresult.

1. An electronic data processing system for automatically determining arisk indicator value based on a number of risk parameters, forevaluating a risk involved with performing a transaction between aclient and a transaction provider in said data processing system, thedata processing system comprising: a first input for inputting clientinput data relating to said client; a first database for storing saidclient input data; a computing processing module; an output foroutputting said risk indicator value; said first database for assemblingand storing said client input data in a predetermined number of clientinput data files in said first database; said computing processingmodule for reading client input data from at least one of said clientinput data files of said first database into said computing processingmodule; a first calculating unit for determining risk parameter valuesfor a number of predefined risk parameters from said client input data,for evaluating each risk parameter value using an associated evaluationfunction to determine an evaluation value of said risk parameter and forcomparing each evaluation value with at least one threshold value todetermine whether the associated risk parameter is critical, uncritical,or neutral for the risk involved with performing said transaction; asecond calculating unit for calculating a risk density for eachnon-neutral risk parameter, for aggregating the risk densities ofnon-neutral risk parameters of at least one redefined set of riskparameters to determine a common risk density of said set of riskparameters, for generating an overall risk probability, based onpredetermined weights and partial risk probabilities for said sets ofrisk parameters, for calculating a net utilisation as the differencebetween an utilisation in said transaction and a value of collateralsavailable for said transaction at a certain point in time; and forcalculating said risk indicator value as the product of said netutilisation and said overall risk probability.
 2. The system of claim 1wherein said second calculating means unit integrates a risk densityfunction using said evaluation value of the respective non-neutral riskparameter.
 3. The system of claim 1 wherein said second calculating unitgenerates a partial risk probability from the common risk density, foreach of a number of risk categories, and said multiples partial riskprobability with an assigned category weight, for each of saidcategories, to determine the overall risk probability as a weightedaverage value of said partial risk probabilities.
 4. The system of oneof claim 1, further comprising a second input for reading thresholdvalues a, b and stretching parameters c, d of the evaluation function;and a first combiner for deriving said evaluation function for each ofsaid risk parameters based on said threshold values and stretchingparameters.
 5. The system of claim 4, further comprising a secondcombiner for deriving said risk density function for each risk parameterwhich has been determined not to be neutral.
 6. The system of claim 1,comprising a second database for storing said risk indicator value. 7.The system of claim 1, comprising a second input for inputtingpredetermined threshold values a, b and stretching parameters c, d froman external source for determining said evaluation functions.
 8. Thesystem of claim 4, comprising a third database for storing saidthreshold values a, b and stretching parameters c, d.
 9. The system ofclaim 1, wherein said client input data comprise personal client data,historical account data and historical credit data of the client,wherein the historical data is assembled in said first database in anumber of files corresponding to a certain number of past months foreach of a number of data types.
 10. A method of using an electronic dataprocessing system for automatically determining a risk indicator valuebased on a number of risk parameters, for evaluating a risk involvedwith performing a transaction between a client and a transactionprovider in said data processing system, the data processing systemincluding a first input for inputting client input data relating to saidclient; a first database for storing said client input data; a computingprocessing module for reading said client input data from said firstdatabase, and calculating said risk indicator value; an output foroutputting said risk indicator value; the method comprising the stepsof: collecting and inputting into said first database client input data;assembling and storing said client input data in a predetermined numberof client input data files in said first database; reading client inputdata from at least one of said client input data files of said firstdatabase into said computing processing module; determining riskparameter values (x) for a predetermined number of redefined riskparameters from said client input data; determining an evaluationfunction for each of said risk parameters; evaluating each riskparameter using the associated evaluation function to determine anevaluation value of said risk parameter; comparing each evaluation valuewith at least one threshold value to determine whether the associatedrisk parameter is critical, uncritical, or neutral for the risk involvedwith performing said transaction; determining a risk density functionfor each risk parameter which has been determined to be not neutral;aggregating the risk densities of at least one redefined set of riskparameters to a common risk density to determine a partial riskprobability for said set of risk parameters; determining a weight forsaid set of risk parameters; generating an overall risk probability,based on said determined risk weights and partial risk probabilities foreach set of risk parameters; calculating a net utilisation as thedifference between a utilisation in said transaction and a value ofcollaterals available for said transaction at a certain point in time;and calculating said risk indicator value as the product of said netutilisation and said overall risk probability.
 11. The method of claim10, wherein said step of aggregating comprises separately aggregatingthe risk densities of non-neutral risk parameters of a number of sets ofrisk parameters to determine a partial risk probability for each of saidsets.
 12. The method of claim 11, wherein a weighted average value ofall said partial risk probabilities is generated to determine an overallrisk probability for calculating said risk indicator value.
 13. Themethod of claim 10, wherein the risk indicator value is output to asecond database for storing said risk indicator value.
 14. The method ofclaim 10, wherein said evaluation function for each of said riskparameters is specifically determined.
 15. The method of claim 14,wherein said threshold values and stretching parameters are input intothe data processing system from an external source.
 16. The method ofclaim 10, wherein said client input data comprise personal client data,historical account data and historical credit data of the client,wherein the historical data is assembled in a number of filescorresponding to up to 36 past months for each of a number of datatypes.
 17. The method of claim 10, wherein said amount claimed in saidtransaction and said value of collaterals relate to respective amountsof monetary units.
 18. The method of claim 10, wherein the riskprobability is expressed as a real number between 0 and 1, inclusive,and the risk indicator value is a non-negative real number.
 19. Themethod of claim 10, wherein the risk indicator values of a group ofclients are aggregated to form a higher level risk indicator value. 20.The method of claim 10 wherein each set of risk parameters correspondsto one category of client-related values.
 21. The method of claim 10wherein client input data are read from each client input data file by asingle access to said file.
 22. Computer program product includingprogram code for performing the method of claim 10 when run on a dataprocessing device.